{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/saheedniyi02/fpl-ai/blob/master/train_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "raKucNuxIcQd"
      },
      "outputs": [],
      "source": [
        "forward_statistics =['value', 'was_home', 'last_season_position', 'percent_value',\n",
        "       'position rank', 'goals_scored_ex', 'assists_ex', 'total_points_ex',\n",
        "       'minutes_ex', 'goals_conceded_ex', 'creativity_ex', 'influence_ex',\n",
        "       'threat_ex', 'bonus_ex', 'bps_ex', 'ict_index_ex', 'now_cost_ex', 'GW', 'opponent_last_season_position',\n",
        "        'mean assists 3','mean bonus 3', 'mean bps 3','mean creativity 3', 'mean goals_scored 3',\n",
        "       'mean ict_index 3', 'mean influence 3', 'mean minutes 3', 'mean penalties_missed 3',  'mean threat 3',\n",
        "       'mean total_points 3','mean value 3', 'mean match_result 3', 'std bps 3', 'std creativity 3',\n",
        "       'std ict_index 3', 'std influence 3', 'std minutes 3',\n",
        "       'std threat 3', 'std total_points 3', 'std value 3']\n",
        "\n",
        "leak_columns = [\n",
        "    \"name\",\n",
        "    \"team\",\n",
        "]  # columns that shouldnt be used in training fir fear of data leakage\n",
        "\n",
        "\n",
        "dropped_columns = [\n",
        "    \"season\",\n",
        "    \"opponent\",\n",
        "    \"match_result\",\n",
        "   # \"position\",\n",
        "    \"assists\",\n",
        "    \"penalties_missed\",\n",
        "    \"bonus\",\n",
        "    \"bps\",\n",
        "    \"clean_sheets\",\n",
        "    \"creativity\",\n",
        "    \"goals_conceded\",\n",
        "    \"goals_scored\",\n",
        "    \"ict_index\",\n",
        "    \"influence\",\n",
        "    \"own_goals\",\n",
        "    \"penalties_saved\",\n",
        "    \"red_cards\",\n",
        "    \"saves\",\n",
        "    \"selected\",\n",
        "    \"threat\",\n",
        "    \"transfers_balance\",\n",
        "    \"transfers_in\",\n",
        "    \"transfers_out\",\n",
        "    \"yellow_cards\",\n",
        "    \"team Goal scored\",\n",
        "    \"team Goal conceded\"\n",
        "]  # \"value\",\n",
        "\n",
        "midfielder_statistics =['value', 'was_home', 'last_season_position', 'percent_value',\n",
        "       'position rank', 'goals_scored_ex', 'assists_ex', 'total_points_ex',\n",
        "       'minutes_ex', 'goals_conceded_ex', 'creativity_ex', 'influence_ex',\n",
        "       'threat_ex', 'bonus_ex', 'bps_ex', 'ict_index_ex', 'now_cost_ex', 'GW', 'opponent_last_season_position',\n",
        "        'mean assists 3','mean bonus 3', 'mean bps 3','mean creativity 3', 'mean goals_scored 3',\n",
        "       'mean ict_index 3', 'mean influence 3', 'mean minutes 3', 'mean penalties_missed 3',  'mean threat 3',\n",
        "       'mean total_points 3','mean value 3', 'mean match_result 3', 'std bps 3', 'std creativity 3',\n",
        "       'std ict_index 3', 'std influence 3', 'std minutes 3',\n",
        "       'std threat 3', 'std total_points 3', 'std value 3']\n",
        "\n",
        "goalkeeper_statistics = ['value', 'was_home', 'last_season_position', 'percent_value',\n",
        "       'position rank', 'total_points_ex', 'minutes_ex', 'goals_conceded_ex',\n",
        "       'bonus_ex', 'bps_ex', 'ict_index_ex', 'clean_sheets_ex',\n",
        "       'red_cards_ex', 'now_cost_ex', 'GW', 'opponent_last_season_position',\n",
        "       'mean bonus 3', 'mean bps 3', 'mean clean_sheets 3', 'mean goals_conceded 3',\n",
        "       'mean ict_index 3',  'mean minutes 3',\n",
        "       'mean own_goals 3',  'mean penalties_saved 3',\n",
        "        'mean saves 3',  'mean threat 3',\n",
        "       'mean total_points 3',\n",
        "       'mean value 3', 'mean match_result 3', 'std bps 3',\n",
        "       'std ict_index 3', 'std influence 3', 'std minutes 3',\n",
        "       'std threat 3', 'std total_points 3', 'std value 3']\n",
        "\n",
        "statistics =['value', 'position','was_home', 'last_season_position', 'percent_value',\n",
        "       'position rank', 'goals_scored_ex', 'assists_ex', 'total_points_ex',\n",
        "       'minutes_ex', 'goals_conceded_ex', 'creativity_ex', 'influence_ex',\n",
        "       'threat_ex', 'bonus_ex', 'bps_ex', 'ict_index_ex', 'clean_sheets_ex',\n",
        "       'yellow_cards_ex','now_cost_ex', 'GW', 'opponent_last_season_position', 'mean assists 3',\n",
        "       'mean bonus 3', 'mean bps 3', 'mean clean_sheets 3',\n",
        "       'mean creativity 3', 'mean goals_conceded 3', 'mean goals_scored 3',\n",
        "       'mean ict_index 3', 'mean influence 3', 'mean minutes 3',\n",
        "       'mean own_goals 3',\n",
        "       'mean red_cards 3',  'mean threat 3','mean total_points 3',\n",
        "       'mean value 3', 'mean match_result 3', 'std bps 3', 'std creativity 3',\n",
        "       'std ict_index 3', 'std influence 3', 'std minutes 3',\n",
        "       'std threat 3', 'std total_points 3', 'std value 3','mean saves 3','mean assists all',\n",
        "       'mean bonus all', 'mean bps all', 'mean clean_sheets all',\n",
        "       'mean creativity all', 'mean goals_conceded all', 'mean goals_scored all',\n",
        "       'mean ict_index all', 'mean influence all', 'mean minutes all',\n",
        "       'mean own_goals all',\n",
        "       'mean red_cards all',  'mean threat all','mean total_points all',\n",
        "       'mean value all', 'mean match_result all',\n",
        "       'mean team Goal scored 3','mean team Goal scored all','mean team Goal conceded 3','mean team Goal conceded all',\"ratio_goal_scored all\",\"ratio_goal_scored 3\",\n",
        "       'opp mean team Goal scored 3','opp mean team Goal conceded 3','opp mean team Goal scored all','opp mean team Goal conceded all',\"opp mean match_result all\"]\n",
        "\n",
        "\n",
        "date_cols=[\"day_of week\",\"month\",\"hour\",\"week\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "n42gWU8iME6S"
      },
      "outputs": [],
      "source": [
        "def convert_minutes(val):\n",
        "    \"\"\"CONVERTS MINUTES TO A CATEGORICAL OUTPUT\"\"\"\n",
        "    if val > 10:\n",
        "        return 1\n",
        "    else:\n",
        "        return 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_NfkZfXAnuRr",
        "outputId": "96fd14fb-21c0-4318-a883-589d7915a124"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting catboost\n",
            "  Downloading catboost-1.2.2-cp310-cp310-manylinux2014_x86_64.whl (98.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.7/98.7 MB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost) (0.20.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from catboost) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.23.5)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.5.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from catboost) (1.11.3)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost) (5.15.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost) (1.16.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2023.3.post1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.1.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (4.43.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (3.1.1)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->catboost) (8.2.3)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-1.2.2\n"
          ]
        }
      ],
      "source": [
        "pip install catboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "4fUTYkPMKCnU"
      },
      "outputs": [],
      "source": [
        "from hyperopt import tpe,hp,fmin,STATUS_OK,Trials\n",
        "from hyperopt.pyll.base import scope"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "_x6NGUGwIo3O"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import warnings\n",
        "import os\n",
        "\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.ensemble import (\n",
        "    RandomForestClassifier,\n",
        "    RandomForestRegressor,\n",
        "    GradientBoostingRegressor,\n",
        ")\n",
        "from sklearn.preprocessing import StandardScaler, RobustScaler, MinMaxScaler\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.linear_model import LinearRegression, Lasso, Ridge\n",
        "from sklearn.metrics import (\n",
        "    mean_squared_error,\n",
        "    mean_absolute_error,\n",
        "    confusion_matrix,\n",
        "    accuracy_score,\n",
        "    f1_score,\n",
        ")\n",
        "from lightgbm import LGBMRegressor, LGBMClassifier\n",
        "from catboost import CatBoostClassifier, CatBoostRegressor\n",
        "from sklearn.model_selection import StratifiedKFold,KFold"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "NLHG8cabKXAo"
      },
      "outputs": [],
      "source": [
        "gameweek=10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "KAEVPF2eKiR2"
      },
      "outputs": [],
      "source": [
        "train = pd.read_csv(\"/content/drive/MyDrive/Fplpredict /cleaned_dataset/cleaned_previous_seasons.csv\",index_col=0)#/content/drive/MyDrive/Fplpredict /cleaned_previous_seasons.csv\", index_col=0)\n",
        "old_gameweek_cleaned = []\n",
        "for i in range(1, gameweek):\n",
        "    old_gameweek_cleaned.append(pd.read_csv(f\"/content/drive/MyDrive/Fplpredict /cleaned_dataset/2023-24/GW{i}.csv\"))\n",
        "old_gameweeks = pd.concat(old_gameweek_cleaned)[train.columns]\n",
        "train = pd.concat([train, old_gameweeks])\n",
        "\n",
        "# data for current gameweek we want to predict on\n",
        "test = pd.read_csv(f\"/content/drive/MyDrive/Fplpredict /cleaned_dataset/2023-24/GW{gameweek}.csv\", index_col=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ldHF-c7SKu67",
        "outputId": "f373d7e3-511c-4a51-a5c1-a0d38983d2e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Folder /content/drive/MyDrive/Fplpredict /predicted_dataset/GW10 already exists\n"
          ]
        }
      ],
      "source": [
        "#CREATE GAMEWEEK PREDICTIONS FOLDER\n",
        "path = f\"/content/drive/MyDrive/Fplpredict /predicted_dataset/GW{gameweek}\"\n",
        "\n",
        "# create new single directory\n",
        "# check whether directory already exists\n",
        "if not os.path.exists(path):\n",
        "    os.mkdir(path)\n",
        "    print(\"Folder %s created!\" % path)\n",
        "else:\n",
        "    print(\"Folder %s already exists\" % path)\n",
        "\n",
        "# data from current season but previous gameweeks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGLtePuRjy7Y",
        "outputId": "64fa7c9e-daa3-45c6-c5df-8947ba4706f5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MID    38456\n",
              "DEF    31804\n",
              "FWD    11640\n",
              "GK      9522\n",
              "GKP      761\n",
              "Name: position, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "train[\"position\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "bsJ6_Ruej9Gl"
      },
      "outputs": [],
      "source": [
        "train[\"position\"]=train[\"position\"].replace({\"GKP\":\"GK\" })"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "FGI39XN-kYzz"
      },
      "outputs": [],
      "source": [
        "test = test[train.columns]\n",
        "train_copy = train.copy()\n",
        "test_copy = test.copy()\n",
        "\n",
        "train[\"index\"] = train[\"name\"] + train[\"kickoff_time\"].astype(\"str\")\n",
        "train.drop_duplicates(\"index\", keep=\"last\", inplace=True)\n",
        "train= train.set_index(\"index\")\n",
        "train[\"date\"]=pd.to_datetime(train[\"kickoff_time\"])\n",
        "train[\"day_of week\"]=train[\"date\"].dt.day_name\n",
        "train[\"month\"]=train[\"date\"].dt.month\n",
        "train[\"hour\"]=train[\"date\"].dt.hour\n",
        "train[\"week\"]=train[\"date\"].dt.week\n",
        "train.drop([\"kickoff_time\",\"date\"], axis=1, inplace=True)\n",
        "\n",
        "test[\"index\"] = test[\"name\"] + test[\"kickoff_time\"].astype(\"str\")\n",
        "test= test.set_index(\"index\")\n",
        "test[\"date\"]=pd.to_datetime(test[\"kickoff_time\"])\n",
        "test[\"day_of week\"]=test[\"date\"].dt.day_name\n",
        "test[\"month\"]=test[\"date\"].dt.month\n",
        "test[\"hour\"]=test[\"date\"].dt.hour\n",
        "test[\"week\"]=test[\"date\"].dt.week\n",
        "test.drop([\"kickoff_time\",\"date\"], axis=1, inplace=True)\n",
        "train[\"minutes\"] = train[\"minutes\"].apply(convert_minutes)\n",
        "\n",
        "target = train[[\"minutes\", \"GW\",\"position\" ]]\n",
        "train.drop([\"total_points\", \"minutes\"], axis=1, inplace=True)\n",
        "test.drop([\"total_points\", \"minutes\"], axis=1, inplace=True)\n",
        "train.drop(dropped_columns, axis=1, inplace=True)\n",
        "test.drop(dropped_columns, axis=1, inplace=True)\n",
        "\n",
        "\n",
        "# Convert categorical columns to numerical\n",
        "for col in train.columns:\n",
        "    if train[col].dtype == \"object\":\n",
        "        if col not in [\"team\", \"name\",\"position\"]:\n",
        "            train[col] = pd.factorize(train[col])[0]\n",
        "            test[col] = pd.factorize(test[col])[0]\n",
        "\n",
        "train[\"was_home\"] = train[\"was_home\"].replace({True: 0, False: 1})\n",
        "test[\"was_home\"] = test[\"was_home\"].replace({True: 0, False: 1})\n",
        "\n",
        "train = train[statistics + leak_columns+date_cols]\n",
        "test = test[statistics + leak_columns+date_cols]\n",
        "\n",
        "x, val, y, y_val = train_test_split(\n",
        "    train.drop(leak_columns, axis=1),\n",
        "    target[\"minutes\"],\n",
        "    test_size=0.1,\n",
        "    random_state=0,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Pc5EqQqLn8ZN"
      },
      "outputs": [],
      "source": [
        "params={'colsample_bylevel': 0.8070621518153563, 'learning_rate': 0.04765984972709895, 'max_depth': 7, 'reg_lambda': 5, 'scale_pos_weight': 2.5,'subsample': 0.6794390204583894}\n",
        "model=CatBoostClassifier(**params,cat_features=[\"position\"],random_state=0,early_stopping_rounds=500,use_best_model=True,verbose=500,n_estimators=10000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ORnl_n6fnJut",
        "outputId": "781e1595-7059-4828-f514-76714abca6b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0:\tlearn: 0.6548918\ttest: 0.6549632\tbest: 0.6549632 (0)\ttotal: 386ms\tremaining: 1h 4m 24s\n",
            "500:\tlearn: 0.3003844\ttest: 0.3266284\tbest: 0.3266046 (492)\ttotal: 1m 10s\tremaining: 22m 23s\n",
            "1000:\tlearn: 0.2741653\ttest: 0.3266822\tbest: 0.3261758 (797)\ttotal: 2m 14s\tremaining: 20m 6s\n",
            "Stopped by overfitting detector  (500 iterations wait)\n",
            "\n",
            "bestTest = 0.3261757901\n",
            "bestIteration = 797\n",
            "\n",
            "Shrink model to first 798 iterations.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostClassifier at 0x7950d61e6890>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "model.fit(x, y,eval_set=[(val,y_val)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZD0zoDWto5j0",
        "outputId": "23b3cd81-a9fc-4512-bd9a-3f3c33c03489"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[3820  271]\n",
            " [1081 2992]]\n",
            "starting Accuracy score 0.8343949044585988\n",
            " starting f1 score: 0.8157033805888768\n"
          ]
        }
      ],
      "source": [
        "print(confusion_matrix(model.predict(val), y_val))\n",
        "print(\n",
        "    f\"starting Accuracy score {accuracy_score(model.predict(val), y_val)}\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\" starting f1 score: {f1_score(model.predict(val), y_val)}\"\n",
        ")\n",
        "\n",
        "feature_importance = pd.DataFrame(\n",
        "    {\"column\": x.columns, \"imp\": model.feature_importances_}\n",
        ").sort_values(\n",
        "    \"imp\", ascending=False\n",
        ")  #"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IaWuoInwvtp2"
      },
      "source": [
        "[[3863  267]\n",
        " [ 975 2862]]\n",
        "starting Accuracy score 0.8441069411321702\n",
        " starting f1 score: 0.8217054263565893"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "a0UEd4_cTO4r",
        "outputId": "d743f842-36d0-4c02-fd55-32d308bcf22d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             column        imp\n",
              "31                   mean minutes 3  12.006206\n",
              "56                 mean minutes all   4.429009\n",
              "42                    std minutes 3   4.424185\n",
              "29                 mean ict_index 3   3.527693\n",
              "20                               GW   3.149277\n",
              "0                             value   2.951257\n",
              "51              mean creativity all   2.918791\n",
              "55               mean influence all   2.890636\n",
              "4                     percent_value   2.684590\n",
              "35              mean total_points 3   2.645315\n",
              "5                     position rank   2.590643\n",
              "60            mean total_points all   2.347154\n",
              "61                   mean value all   2.160690\n",
              "52          mean goals_conceded all   2.132516\n",
              "54               mean ict_index all   2.010548\n",
              "49                     mean bps all   1.964515\n",
              "30                 mean influence 3   1.890172\n",
              "3              last_season_position   1.801461\n",
              "1                          position   1.758095\n",
              "66      mean team Goal conceded all   1.670793\n",
              "43                     std threat 3   1.615126\n",
              "77                             week   1.371841\n",
              "24                       mean bps 3   1.345328\n",
              "64        mean team Goal scored all   1.318488\n",
              "59                  mean threat all   1.240764\n",
              "73        opp mean match_result all   1.173817\n",
              "17                  clean_sheets_ex   1.133980\n",
              "9                        minutes_ex   1.048477\n",
              "50            mean clean_sheets all   1.018635\n",
              "75                            month   1.003420\n",
              "72  opp mean team Goal conceded all   0.998519\n",
              "62            mean match_result all   0.974349\n",
              "71    opp mean team Goal scored all   0.945848\n",
              "65        mean team Goal conceded 3   0.930259\n",
              "11                    creativity_ex   0.865671\n",
              "41                  std influence 3   0.853334\n",
              "26                mean creativity 3   0.847830\n",
              "39                 std creativity 3   0.837664\n",
              "63          mean team Goal scored 3   0.827717\n",
              "38                        std bps 3   0.735068\n",
              "19                      now_cost_ex   0.734875\n",
              "48                   mean bonus all   0.719231\n",
              "8                   total_points_ex   0.709128\n",
              "12                     influence_ex   0.705810\n",
              "10                goals_conceded_ex   0.692582\n",
              "16                     ict_index_ex   0.689631\n",
              "27            mean goals_conceded 3   0.689055\n",
              "37              mean match_result 3   0.680407\n",
              "36                     mean value 3   0.648196\n",
              "45                      std value 3   0.645090"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>column</th>\n",
              "      <th>imp</th>\n",
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              "      <th>31</th>\n",
              "      <td>mean minutes 3</td>\n",
              "      <td>12.006206</td>\n",
              "    </tr>\n",
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              "      <th>56</th>\n",
              "      <td>mean minutes all</td>\n",
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              "      <th>42</th>\n",
              "      <td>std minutes 3</td>\n",
              "      <td>4.424185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>mean ict_index 3</td>\n",
              "      <td>3.527693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>GW</td>\n",
              "      <td>3.149277</td>\n",
              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>value</td>\n",
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              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>mean creativity all</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>mean influence all</td>\n",
              "      <td>2.890636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>percent_value</td>\n",
              "      <td>2.684590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>mean total_points 3</td>\n",
              "      <td>2.645315</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>position rank</td>\n",
              "      <td>2.590643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>mean total_points all</td>\n",
              "      <td>2.347154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>mean value all</td>\n",
              "      <td>2.160690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>mean goals_conceded all</td>\n",
              "      <td>2.132516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>mean ict_index all</td>\n",
              "      <td>2.010548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>mean bps all</td>\n",
              "      <td>1.964515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>mean influence 3</td>\n",
              "      <td>1.890172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>last_season_position</td>\n",
              "      <td>1.801461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>position</td>\n",
              "      <td>1.758095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>mean team Goal conceded all</td>\n",
              "      <td>1.670793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>std threat 3</td>\n",
              "      <td>1.615126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>week</td>\n",
              "      <td>1.371841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>mean bps 3</td>\n",
              "      <td>1.345328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>mean team Goal scored all</td>\n",
              "      <td>1.318488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>mean threat all</td>\n",
              "      <td>1.240764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>opp mean match_result all</td>\n",
              "      <td>1.173817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>clean_sheets_ex</td>\n",
              "      <td>1.133980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>minutes_ex</td>\n",
              "      <td>1.048477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>mean clean_sheets all</td>\n",
              "      <td>1.018635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>month</td>\n",
              "      <td>1.003420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>opp mean team Goal conceded all</td>\n",
              "      <td>0.998519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>mean match_result all</td>\n",
              "      <td>0.974349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>opp mean team Goal scored all</td>\n",
              "      <td>0.945848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>mean team Goal conceded 3</td>\n",
              "      <td>0.930259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>creativity_ex</td>\n",
              "      <td>0.865671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>std influence 3</td>\n",
              "      <td>0.853334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>mean creativity 3</td>\n",
              "      <td>0.847830</td>\n",
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              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>std creativity 3</td>\n",
              "      <td>0.837664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>mean team Goal scored 3</td>\n",
              "      <td>0.827717</td>\n",
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              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>std bps 3</td>\n",
              "      <td>0.735068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>now_cost_ex</td>\n",
              "      <td>0.734875</td>\n",
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              "    <tr>\n",
              "      <th>48</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>total_points_ex</td>\n",
              "      <td>0.709128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>influence_ex</td>\n",
              "      <td>0.705810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>goals_conceded_ex</td>\n",
              "      <td>0.692582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>ict_index_ex</td>\n",
              "      <td>0.689631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>mean goals_conceded 3</td>\n",
              "      <td>0.689055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>mean match_result 3</td>\n",
              "      <td>0.680407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>mean value 3</td>\n",
              "      <td>0.648196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>std value 3</td>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "feature_importance.head(50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
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        {
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          "data": {
            "text/plain": [
              "                           column       imp\n",
              "70  opp mean team Goal conceded 3  0.465798\n",
              "18                yellow_cards_ex  0.455093\n",
              "47               mean assists all  0.426588\n",
              "23                   mean bonus 3  0.410647\n",
              "57             mean own_goals all  0.402117\n",
              "58             mean red_cards all  0.379497\n",
              "67          ratio_goal_scored all  0.376669\n",
              "32               mean own_goals 3  0.371388\n",
              "76                           hour  0.363850\n",
              "6                 goals_scored_ex  0.319510\n",
              "7                      assists_ex  0.317864\n",
              "34                  mean threat 3  0.285440\n",
              "2                        was_home  0.284276\n",
              "69    opp mean team Goal scored 3  0.257607\n",
              "68            ratio_goal_scored 3  0.193383\n",
              "53          mean goals_scored all  0.166219\n",
              "46                   mean saves 3  0.086837\n",
              "22                 mean assists 3  0.051928\n",
              "28            mean goals_scored 3  0.012596\n",
              "74                    day_of week  0.000000"
            ],
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              "\n",
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              "      <th>column</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>opp mean team Goal conceded 3</td>\n",
              "      <td>0.465798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>yellow_cards_ex</td>\n",
              "      <td>0.455093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>mean assists all</td>\n",
              "      <td>0.426588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>mean bonus 3</td>\n",
              "      <td>0.410647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>mean own_goals all</td>\n",
              "      <td>0.402117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>mean red_cards all</td>\n",
              "      <td>0.379497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>ratio_goal_scored all</td>\n",
              "      <td>0.376669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>mean own_goals 3</td>\n",
              "      <td>0.371388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>hour</td>\n",
              "      <td>0.363850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>goals_scored_ex</td>\n",
              "      <td>0.319510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>assists_ex</td>\n",
              "      <td>0.317864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>mean threat 3</td>\n",
              "      <td>0.285440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>was_home</td>\n",
              "      <td>0.284276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>opp mean team Goal scored 3</td>\n",
              "      <td>0.257607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>ratio_goal_scored 3</td>\n",
              "      <td>0.193383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>mean goals_scored all</td>\n",
              "      <td>0.166219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>mean saves 3</td>\n",
              "      <td>0.086837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>mean assists 3</td>\n",
              "      <td>0.051928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>mean goals_scored 3</td>\n",
              "      <td>0.012596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>day_of week</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "        document.querySelector('#df-d2253441-a5e0-49c5-b3e3-0dce0c38851c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d2253441-a5e0-49c5-b3e3-0dce0c38851c');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c7e76c49-4599-4b22-a7dc-259e609c6557 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "feature_importance.tail(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_LNgj0zfBh5y",
        "outputId": "5a0e03e6-dfdd-4b5a-f895-4938ef2200af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "index\n",
              "Oriol Romeu Vidal2023-01-14 15:00:00+00:00           0\n",
              "Ellery Balcombe2022-01-11 19:45:00+00:00             0\n",
              "Bukayo Saka2022-11-12 19:45:00+00:00                 1\n",
              "Martin Dubravka2021-12-19 14:00:00+00:00             1\n",
              "Mark Gillespie2022-05-08 15:30:00+00:00              0\n",
              "                                                    ..\n",
              "Rodrigo Hernandez2022-05-08 15:30:00+00:00           1\n",
              "Romain Perraud2023-01-21 15:00:00+00:00              1\n",
              "Carlos Henrique Casimiro2022-11-13 16:30:00+00:00    1\n",
              "Kevin Long2021-03-06 12:30:00+00:00                  0\n",
              "Cheikh Diaby2021-01-26 20:15:00+00:00                0\n",
              "Name: minutes, Length: 8164, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "y_val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "mZrjfnMwpYAP"
      },
      "outputs": [],
      "source": [
        "test_copy[\"minutes\"] = model.predict(test.drop(leak_columns, axis=1))\n",
        "test_copy[leak_columns + [\"minutes\"]].to_csv(\n",
        "    f\"minutes.csv\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "Crlq1n1JBd5b"
      },
      "outputs": [],
      "source": [
        "predicted_minutes=model.predict(val)\n",
        "val_=pd.DataFrame({\"ind\":val.index,\"actul_minutes\":y_val,\"predicted_minutes\":predicted_minutes,\"position\":val[\"position\"]})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DBc8-KNlDbeG",
        "outputId": "e64dd990-35b7-4f28-b37c-d3ac024ee6ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "STARTING GOALKEEPERS PERFORMANCE!!!\n",
            "[[618  36]\n",
            " [ 18 241]]\n",
            "starting Accuracy score 0.940854326396495\n",
            " starting f1 score: 0.8992537313432836\n"
          ]
        }
      ],
      "source": [
        "print(f\"STARTING GOALKEEPERS PERFORMANCE!!!\")\n",
        "a=val_[val_[\"position\"]==\"GK\"][\"actul_minutes\"]\n",
        "b=val_[val_[\"position\"]==\"GK\"][\"predicted_minutes\"]\n",
        "print(confusion_matrix(a,b))\n",
        "print(\n",
        "    f\"starting Accuracy score {accuracy_score(a,b)}\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\" starting f1 score: {f1_score(a,b)}\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z3zLvWZmC5Vh"
      },
      "source": [
        "STARTING GOALKEEPERS PERFORMANCE!!!\n",
        "[[583  21]\n",
        " [ 24 216]]\n",
        "starting Accuracy score 0.9466824644549763\n",
        " starting f1 score: 0.9056603773584907\n",
        "\n",
        "STARTING DEFENDERS PERFORMANCE!!!\n",
        "[[1449  208]\n",
        " [ 200  915]]\n",
        "starting Accuracy score 0.8528138528138528\n",
        " starting f1 score: 0.817694369973190\n",
        "\n",
        "STARTING MIDFIELDERS PERFORMANCE!!!\n",
        "[[1595  288]\n",
        " [ 235 1195]]\n",
        "starting Accuracy score 0.8421370359191066\n",
        " starting f1 score: 0.820460006865774\n",
        "\n",
        "STARTING FORWARDS PERFORMANCE!!!\n",
        "[[497  88]\n",
        " [ 69 325]]\n",
        "starting Accuracy score 0.839632277834525\n",
        " starting f1 score: 0.8054522924411399"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FXFtoCzvECJZ",
        "outputId": "d176ee4f-e3ff-4d82-f5c7-b2a6ffbb040c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "STARTING DEFENDERS PERFORMANCE!!!\n",
            "[[1286  404]\n",
            " [ 121  992]]\n",
            "starting Accuracy score 0.8127006778451659\n",
            " starting f1 score: 0.7907532881626147\n"
          ]
        }
      ],
      "source": [
        "print(f\"STARTING DEFENDERS PERFORMANCE!!!\")\n",
        "a=val_[val_[\"position\"]==\"DEF\"][\"actul_minutes\"]\n",
        "b=val_[val_[\"position\"]==\"DEF\"][\"predicted_minutes\"]\n",
        "print(confusion_matrix(a,b))\n",
        "print(\n",
        "    f\"starting Accuracy score {accuracy_score(a,b)}\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\" starting f1 score: {f1_score(a,b)}\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lhgC0BAdD7xZ",
        "outputId": "f3de6e5e-74d7-4f7c-e22b-21be0d28af4f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "STARTING MIDFIELDERS PERFORMANCE!!!\n",
            "[[1443  499]\n",
            " [ 113 1389]]\n",
            "starting Accuracy score 0.8222996515679443\n",
            " starting f1 score: 0.8194690265486725\n"
          ]
        }
      ],
      "source": [
        "print(f\"STARTING MIDFIELDERS PERFORMANCE!!!\")\n",
        "a=val_[val_[\"position\"]==\"MID\"][\"actul_minutes\"]\n",
        "b=val_[val_[\"position\"]==\"MID\"][\"predicted_minutes\"]\n",
        "print(confusion_matrix(a,b))\n",
        "print(\n",
        "    f\"starting Accuracy score {accuracy_score(a,b)}\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\" starting f1 score: {f1_score(a,b)}\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OlE-qPpSEFOA",
        "outputId": "285f3d00-99b5-49f5-98c6-c06050571ecb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "STARTING FORWARDS PERFORMANCE!!!\n",
            "[[473 142]\n",
            " [ 19 370]]\n",
            "starting Accuracy score 0.8396414342629482\n",
            " starting f1 score: 0.8213096559378469\n"
          ]
        }
      ],
      "source": [
        "print(f\"STARTING FORWARDS PERFORMANCE!!!\")\n",
        "a=val_[val_[\"position\"]==\"FWD\"][\"actul_minutes\"]\n",
        "b=val_[val_[\"position\"]==\"FWD\"][\"predicted_minutes\"]\n",
        "print(confusion_matrix(a,b))\n",
        "print(\n",
        "    f\"starting Accuracy score {accuracy_score(a,b)}\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\" starting f1 score: {f1_score(a,b)}\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QidLuN2qpwHI",
        "outputId": "1d9f4dad-5139-44c9-e662-0c950fc82fe9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MID    148\n",
              "DEF    104\n",
              "FWD     42\n",
              "GKP     22\n",
              "Name: position, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "test_copy[test_copy[\"minutes\"]==1][\"position\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "E5XuG1jhpt4s"
      },
      "outputs": [],
      "source": [
        "train = train_copy[train_copy[\"minutes\"] > 0]\n",
        "test = test_copy[test_copy[\"minutes\"] > 0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "L5DlE5tLsWo8"
      },
      "outputs": [],
      "source": [
        "\n",
        "# predict points\n",
        "train[\"index\"] = train[\"name\"] + train[\"kickoff_time\"].astype(\"str\")\n",
        "train.drop_duplicates(\"index\", keep=\"last\", inplace=True)\n",
        "train= train.set_index(\"index\")\n",
        "train[\"date\"]=pd.to_datetime(train[\"kickoff_time\"])\n",
        "train[\"day_of week\"]=train[\"date\"].dt.day_name\n",
        "train[\"month\"]=train[\"date\"].dt.month\n",
        "train[\"hour\"]=train[\"date\"].dt.hour\n",
        "train[\"week\"]=train[\"date\"].dt.week\n",
        "train.drop([\"kickoff_time\",\"date\"], axis=1, inplace=True)\n",
        "\n",
        "test[\"index\"] = test[\"name\"] + test[\"kickoff_time\"].astype(\"str\")\n",
        "test = test.set_index(\"index\")\n",
        "test[\"date\"]=pd.to_datetime(test[\"kickoff_time\"])\n",
        "test[\"day_of week\"]=test[\"date\"].dt.day_name\n",
        "test[\"month\"]=test[\"date\"].dt.month\n",
        "test[\"hour\"]=test[\"date\"].dt.hour\n",
        "test[\"week\"]=test[\"date\"].dt.week\n",
        "test.drop([\"kickoff_time\",\"date\"], axis=1, inplace=True)\n",
        "\n",
        "target = train[[\"total_points\", \"GW\",\"position\" ]]\n",
        "train.drop([\"total_points\", \"minutes\"], axis=1, inplace=True)\n",
        "test.drop([\"total_points\", \"minutes\"], axis=1, inplace=True)\n",
        "train.drop(dropped_columns, axis=1, inplace=True)\n",
        "test.drop(dropped_columns, axis=1, inplace=True)\n",
        "\n",
        "\n",
        "for col in train.columns:\n",
        "    if train[col].dtype == \"object\":\n",
        "        if col not in [\"team\", \"name\",\"position\"]:\n",
        "            train[col] = pd.factorize(train[col])[0]\n",
        "            test[col] = pd.factorize(test[col])[0]\n",
        "\n",
        "train[\"position\"]=train[\"position\"].astype(\"category\")\n",
        "test[\"position\"]=test[\"position\"].astype(\"category\")\n",
        "train[\"was_home\"] = train[\"was_home\"].replace({True: 0, False: 1})\n",
        "test[\"was_home\"] = test[\"was_home\"].replace({True: 0, False: 1})\n",
        "\n",
        "train = train[statistics + leak_columns+date_cols]\n",
        "test= test[statistics + leak_columns+date_cols]\n",
        "\n",
        "x, val, y, y_val = train_test_split(\n",
        "    train.drop(leak_columns, axis=1),\n",
        "    target[\"total_points\"],\n",
        "    test_size=0.1,\n",
        "    random_state=0,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xNZGBu2FhgFj",
        "outputId": "c2370e4b-a4cd-4551-b96a-a837458b120d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "[2.75799904510382, 2.6652997142930492, 2.6832936632856086, 2.8264025093811855, 2.8638436004667875, 2.7048447357279364, 2.7494556057432997, 2.6377462697198215]\n",
            "[1.5229543294014234, 1.9308316970996955, 1.892765585734737, 1.9056027047168056, 1.6943890843424334, 1.7993653432035002, 1.5870089580031228, 1.9578922425800178]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import KFold\n",
        "\n",
        "#cross_validator to splite the data into folds\n",
        "folds=KFold(n_splits=8,shuffle=True,random_state=0)\n",
        "\n",
        "#a dataframe to store the predictions made by each fold\n",
        "predictions_df=pd.DataFrame()\n",
        "\n",
        "#list to save the mean absolute errors from validatingon each folds\n",
        "rmse_val=[]\n",
        "rmse_X=[]\n",
        "\n",
        "#a simple catboost regressor\n",
        "model=LGBMRegressor(**{'colsample_bytree': 0.4199299182268318, 'learning_rate': 0.0032874466037521254, 'max_depth': 9, 'min_split_gain': 0.5685369160138952, 'num_leaves': 99, 'reg_alpha': 0.5621526419488447, 'reg_lambda': 0, 'subsample': 0.6534153111773866}, verbose=-50,random_state=0,early_stopping_rounds=200,n_estimators=10000)\n",
        "\n",
        "#train model, make predictions and check the validation accuracy on  each fold\n",
        "for i,(train_index,test_index) in enumerate(folds.split(train.drop(leak_columns, axis=1),target[\"total_points\"])):\n",
        "    train_fold=train.drop(leak_columns, axis=1).iloc[train_index]\n",
        "    val_fold=train.drop(leak_columns, axis=1).iloc[test_index]\n",
        "    y_fold=target[\"total_points\"].iloc[train_index]\n",
        "    y_val_fold=target[\"total_points\"].iloc[test_index]\n",
        "\n",
        "\n",
        "    model.fit(train_fold,y_fold,eval_set=[(val_fold,y_val_fold)])\n",
        "    print(i+1)\n",
        "    prediction=model.predict(test.drop(leak_columns, axis=1))\n",
        "    predictions_df[i]=prediction\n",
        "    rmse_val.append(mean_squared_error(model.predict(val_fold),y_val_fold,squared=False))\n",
        "    rmse_X.append(mean_squared_error(model.predict(train_fold),y_fold,squared=False))\n",
        "print(rmse_val)\n",
        "print(rmse_X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R6NBjO7yzPOT",
        "outputId": "aba2700f-3bf3-4d76-b98c-d83f602aba81"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.7361106429651887\n",
            "1.786351243135217\n"
          ]
        }
      ],
      "source": [
        "print(np.mean(rmse_val))\n",
        "print(np.mean(rmse_X))\n",
        "#2.7575293874474336\n",
        "#2.0174134721607295"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "475Nb44Amx4V"
      },
      "source": [
        "2.7320675301713573\n",
        "1.6917027774360753"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-kDj3_xeq8Ia",
        "outputId": "be804606-b981-46e4-e9b3-2d50216e1b75"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      2.924173\n",
              "1      2.924595\n",
              "2      2.814342\n",
              "3      2.395066\n",
              "4      3.853160\n",
              "         ...   \n",
              "311    2.302227\n",
              "312    3.181070\n",
              "313    2.069684\n",
              "314    1.178730\n",
              "315    2.053070\n",
              "Name: 0, Length: 316, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "predictions_df[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "W_b08Rfnp76c"
      },
      "outputs": [],
      "source": [
        "test[\"points\"]=np.mean(predictions_df, axis=1).values\n",
        "\n",
        "test[leak_columns + [\"points\", \"value\"]].sort_values(\n",
        "    \"points\", ascending=False\n",
        ").to_csv(\"points.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "g-ibL8BVAR2e",
        "outputId": "c58bf3f6-fd8b-4fa9-8505-8047b5417e6e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                  name  \\\n",
              "index                                                                    \n",
              "Mohamed Salah2023-10-29T14:00:00Z                        Mohamed Salah   \n",
              "Martin Ødegaard2023-10-28T14:00:00Z                    Martin Ødegaard   \n",
              "Bukayo Saka2023-10-28T14:00:00Z                            Bukayo Saka   \n",
              "Son Heung-min2023-10-27T19:00:00Z                        Son Heung-min   \n",
              "Gabriel Martinelli Silva2023-10-28T14:00:00Z  Gabriel Martinelli Silva   \n",
              "James Maddison2023-10-27T19:00:00Z                      James Maddison   \n",
              "Kaoru Mitoma2023-10-29T14:00:00Z                          Kaoru Mitoma   \n",
              "Bryan Mbeumo2023-10-28T11:30:00Z                          Bryan Mbeumo   \n",
              "Diogo Teixeira da Silva2023-10-29T14:00:00Z    Diogo Teixeira da Silva   \n",
              "Jarrod Bowen2023-10-29T13:00:00Z                          Jarrod Bowen   \n",
              "Dominik Szoboszlai2023-10-29T14:00:00Z              Dominik Szoboszlai   \n",
              "\n",
              "                                                points       team  \n",
              "index                                                              \n",
              "Mohamed Salah2023-10-29T14:00:00Z             8.735140  Liverpool  \n",
              "Martin Ødegaard2023-10-28T14:00:00Z           6.533175    Arsenal  \n",
              "Bukayo Saka2023-10-28T14:00:00Z               5.956754    Arsenal  \n",
              "Son Heung-min2023-10-27T19:00:00Z             5.859988      Spurs  \n",
              "Gabriel Martinelli Silva2023-10-28T14:00:00Z  5.840069    Arsenal  \n",
              "James Maddison2023-10-27T19:00:00Z            5.806873      Spurs  \n",
              "Kaoru Mitoma2023-10-29T14:00:00Z              5.523091   Brighton  \n",
              "Bryan Mbeumo2023-10-28T11:30:00Z              4.641319  Brentford  \n",
              "Diogo Teixeira da Silva2023-10-29T14:00:00Z   4.614422  Liverpool  \n",
              "Jarrod Bowen2023-10-29T13:00:00Z              4.555775   West Ham  \n",
              "Dominik Szoboszlai2023-10-29T14:00:00Z        4.537633  Liverpool  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b3696c4c-3aa2-47d4-aab3-e5ac70b01cfa\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>points</th>\n",
              "      <th>team</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>index</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Mohamed Salah2023-10-29T14:00:00Z</th>\n",
              "      <td>Mohamed Salah</td>\n",
              "      <td>8.735140</td>\n",
              "      <td>Liverpool</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Martin Ødegaard2023-10-28T14:00:00Z</th>\n",
              "      <td>Martin Ødegaard</td>\n",
              "      <td>6.533175</td>\n",
              "      <td>Arsenal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bukayo Saka2023-10-28T14:00:00Z</th>\n",
              "      <td>Bukayo Saka</td>\n",
              "      <td>5.956754</td>\n",
              "      <td>Arsenal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Son Heung-min2023-10-27T19:00:00Z</th>\n",
              "      <td>Son Heung-min</td>\n",
              "      <td>5.859988</td>\n",
              "      <td>Spurs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gabriel Martinelli Silva2023-10-28T14:00:00Z</th>\n",
              "      <td>Gabriel Martinelli Silva</td>\n",
              "      <td>5.840069</td>\n",
              "      <td>Arsenal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>James Maddison2023-10-27T19:00:00Z</th>\n",
              "      <td>James Maddison</td>\n",
              "      <td>5.806873</td>\n",
              "      <td>Spurs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kaoru Mitoma2023-10-29T14:00:00Z</th>\n",
              "      <td>Kaoru Mitoma</td>\n",
              "      <td>5.523091</td>\n",
              "      <td>Brighton</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bryan Mbeumo2023-10-28T11:30:00Z</th>\n",
              "      <td>Bryan Mbeumo</td>\n",
              "      <td>4.641319</td>\n",
              "      <td>Brentford</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Diogo Teixeira da Silva2023-10-29T14:00:00Z</th>\n",
              "      <td>Diogo Teixeira da Silva</td>\n",
              "      <td>4.614422</td>\n",
              "      <td>Liverpool</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jarrod Bowen2023-10-29T13:00:00Z</th>\n",
              "      <td>Jarrod Bowen</td>\n",
              "      <td>4.555775</td>\n",
              "      <td>West Ham</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dominik Szoboszlai2023-10-29T14:00:00Z</th>\n",
              "      <td>Dominik Szoboszlai</td>\n",
              "      <td>4.537633</td>\n",
              "      <td>Liverpool</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3696c4c-3aa2-47d4-aab3-e5ac70b01cfa')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b3696c4c-3aa2-47d4-aab3-e5ac70b01cfa button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b3696c4c-3aa2-47d4-aab3-e5ac70b01cfa');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e74a6c59-b93a-46fd-a6ea-721d607b35ba\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e74a6c59-b93a-46fd-a6ea-721d607b35ba')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e74a6c59-b93a-46fd-a6ea-721d607b35ba button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "test[test[\"position\"]==\"MID\"].sort_values(by=\"points\",ascending=False).head(11)[[\"name\",\"points\",\"team\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "R9-XbtdrAeyB",
        "outputId": "b6433746-a570-44ea-83ff-cb49732543c9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                              name    points  \\\n",
              "index                                                                          \n",
              "Kieran Trippier2023-10-28T16:30:00Z                Kieran Trippier  6.195080   \n",
              "Trent Alexander-Arnold2023-10-29T14:00:00Z  Trent Alexander-Arnold  5.014530   \n",
              "Lucas Digne2023-10-29T14:00:00Z                        Lucas Digne  4.710693   \n",
              "Pau Torres2023-10-29T14:00:00Z                          Pau Torres  4.661043   \n",
              "Ezri Konsa Ngoyo2023-10-29T14:00:00Z              Ezri Konsa Ngoyo  4.535804   \n",
              "Andrew Robertson2023-10-29T14:00:00Z              Andrew Robertson  4.213994   \n",
              "Matty Cash2023-10-29T14:00:00Z                          Matty Cash  4.123158   \n",
              "Vladimír Coufal2023-10-29T13:00:00Z                Vladimír Coufal  3.972972   \n",
              "Cristian Romero2023-10-27T19:00:00Z                Cristian Romero  3.945540   \n",
              "Joël Veltman2023-10-29T14:00:00Z                      Joël Veltman  3.920138   \n",
              "\n",
              "                                                   team  \n",
              "index                                                    \n",
              "Kieran Trippier2023-10-28T16:30:00Z           Newcastle  \n",
              "Trent Alexander-Arnold2023-10-29T14:00:00Z    Liverpool  \n",
              "Lucas Digne2023-10-29T14:00:00Z             Aston Villa  \n",
              "Pau Torres2023-10-29T14:00:00Z              Aston Villa  \n",
              "Ezri Konsa Ngoyo2023-10-29T14:00:00Z        Aston Villa  \n",
              "Andrew Robertson2023-10-29T14:00:00Z          Liverpool  \n",
              "Matty Cash2023-10-29T14:00:00Z              Aston Villa  \n",
              "Vladimír Coufal2023-10-29T13:00:00Z            West Ham  \n",
              "Cristian Romero2023-10-27T19:00:00Z               Spurs  \n",
              "Joël Veltman2023-10-29T14:00:00Z               Brighton  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d9c09138-74b1-4fdb-84d2-a3075c51b5a3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>points</th>\n",
              "      <th>team</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>index</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Kieran Trippier2023-10-28T16:30:00Z</th>\n",
              "      <td>Kieran Trippier</td>\n",
              "      <td>6.195080</td>\n",
              "      <td>Newcastle</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Trent Alexander-Arnold2023-10-29T14:00:00Z</th>\n",
              "      <td>Trent Alexander-Arnold</td>\n",
              "      <td>5.014530</td>\n",
              "      <td>Liverpool</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lucas Digne2023-10-29T14:00:00Z</th>\n",
              "      <td>Lucas Digne</td>\n",
              "      <td>4.710693</td>\n",
              "      <td>Aston Villa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pau Torres2023-10-29T14:00:00Z</th>\n",
              "      <td>Pau Torres</td>\n",
              "      <td>4.661043</td>\n",
              "      <td>Aston Villa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ezri Konsa Ngoyo2023-10-29T14:00:00Z</th>\n",
              "      <td>Ezri Konsa Ngoyo</td>\n",
              "      <td>4.535804</td>\n",
              "      <td>Aston Villa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andrew Robertson2023-10-29T14:00:00Z</th>\n",
              "      <td>Andrew Robertson</td>\n",
              "      <td>4.213994</td>\n",
              "      <td>Liverpool</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Matty Cash2023-10-29T14:00:00Z</th>\n",
              "      <td>Matty Cash</td>\n",
              "      <td>4.123158</td>\n",
              "      <td>Aston Villa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vladimír Coufal2023-10-29T13:00:00Z</th>\n",
              "      <td>Vladimír Coufal</td>\n",
              "      <td>3.972972</td>\n",
              "      <td>West Ham</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cristian Romero2023-10-27T19:00:00Z</th>\n",
              "      <td>Cristian Romero</td>\n",
              "      <td>3.945540</td>\n",
              "      <td>Spurs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Joël Veltman2023-10-29T14:00:00Z</th>\n",
              "      <td>Joël Veltman</td>\n",
              "      <td>3.920138</td>\n",
              "      <td>Brighton</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d9c09138-74b1-4fdb-84d2-a3075c51b5a3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d9c09138-74b1-4fdb-84d2-a3075c51b5a3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d9c09138-74b1-4fdb-84d2-a3075c51b5a3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d790bcf8-9056-4b9f-955f-4ce27dc853e1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d790bcf8-9056-4b9f-955f-4ce27dc853e1')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d790bcf8-9056-4b9f-955f-4ce27dc853e1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "test[test[\"position\"]==\"DEF\"].sort_values(by=\"points\",ascending=False).head(10)[[\"name\",\"points\",\"team\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "_b9fjvegAsG7",
        "outputId": "7073a71f-7627-4ab9-ad66-b2a878dca5cb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                    name  \\\n",
              "index                                                                      \n",
              "Emiliano Martínez Romero2023-10-29T14:00:00Z    Emiliano Martínez Romero   \n",
              "David Raya Martin2023-10-28T14:00:00Z                  David Raya Martin   \n",
              "Alisson Ramses Becker2023-10-29T14:00:00Z          Alisson Ramses Becker   \n",
              "Norberto Murara Neto2023-10-28T14:00:00Z            Norberto Murara Neto   \n",
              "Ederson Santana de Moraes2023-10-29T15:30:00Z  Ederson Santana de Moraes   \n",
              "Alphonse Areola2023-10-29T13:00:00Z                      Alphonse Areola   \n",
              "Jason Steele2023-10-29T14:00:00Z                            Jason Steele   \n",
              "Guglielmo Vicario2023-10-27T19:00:00Z                  Guglielmo Vicario   \n",
              "Mark Flekken2023-10-28T11:30:00Z                            Mark Flekken   \n",
              "Nick Pope2023-10-28T16:30:00Z                                  Nick Pope   \n",
              "\n",
              "                                                 points         team  \n",
              "index                                                                 \n",
              "Emiliano Martínez Romero2023-10-29T14:00:00Z   4.385442  Aston Villa  \n",
              "David Raya Martin2023-10-28T14:00:00Z          4.204943      Arsenal  \n",
              "Alisson Ramses Becker2023-10-29T14:00:00Z      3.967894    Liverpool  \n",
              "Norberto Murara Neto2023-10-28T14:00:00Z       3.812593  Bournemouth  \n",
              "Ederson Santana de Moraes2023-10-29T15:30:00Z  3.605701     Man City  \n",
              "Alphonse Areola2023-10-29T13:00:00Z            3.600888     West Ham  \n",
              "Jason Steele2023-10-29T14:00:00Z               3.504677     Brighton  \n",
              "Guglielmo Vicario2023-10-27T19:00:00Z          3.356717        Spurs  \n",
              "Mark Flekken2023-10-28T11:30:00Z               3.235551    Brentford  \n",
              "Nick Pope2023-10-28T16:30:00Z                  3.030811    Newcastle  "
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "test[test[\"position\"]==\"GKP\"].sort_values(by=\"points\",ascending=False).head(10)[[\"name\",\"points\",\"team\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "DhgfhH7AgUW3",
        "outputId": "38d4b894-d287-4898-d483-eb736b280def"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                            name  \\\n",
              "index                                                                              \n",
              "Erling Haaland2023-10-29T15:30:00Z                                Erling Haaland   \n",
              "Ollie Watkins2023-10-29T14:00:00Z                                  Ollie Watkins   \n",
              "Dominic Solanke2023-10-28T14:00:00Z                              Dominic Solanke   \n",
              "Gabriel Fernando de Jesus2023-10-28T14:00:00Z          Gabriel Fernando de Jesus   \n",
              "Darwin Núñez Ribeiro2023-10-29T14:00:00Z                    Darwin Núñez Ribeiro   \n",
              "Julián Álvarez2023-10-29T15:30:00Z                                Julián Álvarez   \n",
              "Yoane Wissa2023-10-28T11:30:00Z                                      Yoane Wissa   \n",
              "João Pedro Junqueira de Jesus2023-10-29T14:00:00Z  João Pedro Junqueira de Jesus   \n",
              "Callum Wilson2023-10-28T16:30:00Z                                  Callum Wilson   \n",
              "Cody Gakpo2023-10-29T14:00:00Z                                        Cody Gakpo   \n",
              "\n",
              "                                                     points         team  \n",
              "index                                                                     \n",
              "Erling Haaland2023-10-29T15:30:00Z                 6.836103     Man City  \n",
              "Ollie Watkins2023-10-29T14:00:00Z                  5.577515  Aston Villa  \n",
              "Dominic Solanke2023-10-28T14:00:00Z                5.046332  Bournemouth  \n",
              "Gabriel Fernando de Jesus2023-10-28T14:00:00Z      4.544171      Arsenal  \n",
              "Darwin Núñez Ribeiro2023-10-29T14:00:00Z           4.181486    Liverpool  \n",
              "Julián Álvarez2023-10-29T15:30:00Z                 3.875503     Man City  \n",
              "Yoane Wissa2023-10-28T11:30:00Z                    3.780475    Brentford  \n",
              "João Pedro Junqueira de Jesus2023-10-29T14:00:00Z  3.535017     Brighton  \n",
              "Callum Wilson2023-10-28T16:30:00Z                  3.529454    Newcastle  \n",
              "Cody Gakpo2023-10-29T14:00:00Z                     3.388202    Liverpool  "
            ],
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              "      <th>Dominic Solanke2023-10-28T14:00:00Z</th>\n",
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              "      <td>Julián Álvarez</td>\n",
              "      <td>3.875503</td>\n",
              "      <td>Man City</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yoane Wissa2023-10-28T11:30:00Z</th>\n",
              "      <td>Yoane Wissa</td>\n",
              "      <td>3.780475</td>\n",
              "      <td>Brentford</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>João Pedro Junqueira de Jesus2023-10-29T14:00:00Z</th>\n",
              "      <td>João Pedro Junqueira de Jesus</td>\n",
              "      <td>3.535017</td>\n",
              "      <td>Brighton</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Callum Wilson2023-10-28T16:30:00Z</th>\n",
              "      <td>Callum Wilson</td>\n",
              "      <td>3.529454</td>\n",
              "      <td>Newcastle</td>\n",
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              "    <tr>\n",
              "      <th>Cody Gakpo2023-10-29T14:00:00Z</th>\n",
              "      <td>Cody Gakpo</td>\n",
              "      <td>3.388202</td>\n",
              "      <td>Liverpool</td>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-18b04cbe-753b-4057-96ab-04ec7268ff71 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-18b04cbe-753b-4057-96ab-04ec7268ff71');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f6b0da8b-4deb-4b19-b6f9-0477d47dfee7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f6b0da8b-4deb-4b19-b6f9-0477d47dfee7')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f6b0da8b-4deb-4b19-b6f9-0477d47dfee7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "test[test[\"position\"]==\"FWD\"].sort_values(by=\"points\",ascending=False).head(10)[[\"name\",\"points\",\"team\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UCBjSKR_uf6m",
        "outputId": "05cf4abf-4d6c-4938-abfa-61aa0cebd817"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "index\n",
              "Mohamed Salah2023-10-29T14:00:00Z                   8.735140\n",
              "Erling Haaland2023-10-29T15:30:00Z                  6.836103\n",
              "Martin Ødegaard2023-10-28T14:00:00Z                 6.533175\n",
              "Kieran Trippier2023-10-28T16:30:00Z                 6.195080\n",
              "Bukayo Saka2023-10-28T14:00:00Z                     5.956754\n",
              "Son Heung-min2023-10-27T19:00:00Z                   5.859988\n",
              "Gabriel Martinelli Silva2023-10-28T14:00:00Z        5.840069\n",
              "James Maddison2023-10-27T19:00:00Z                  5.806873\n",
              "Ollie Watkins2023-10-29T14:00:00Z                   5.577515\n",
              "Kaoru Mitoma2023-10-29T14:00:00Z                    5.523091\n",
              "Dominic Solanke2023-10-28T14:00:00Z                 5.046332\n",
              "Trent Alexander-Arnold2023-10-29T14:00:00Z          5.014530\n",
              "Lucas Digne2023-10-29T14:00:00Z                     4.710693\n",
              "Pau Torres2023-10-29T14:00:00Z                      4.661043\n",
              "Bryan Mbeumo2023-10-28T11:30:00Z                    4.641319\n",
              "Diogo Teixeira da Silva2023-10-29T14:00:00Z         4.614422\n",
              "Jarrod Bowen2023-10-29T13:00:00Z                    4.555775\n",
              "Gabriel Fernando de Jesus2023-10-28T14:00:00Z       4.544171\n",
              "Dominik Szoboszlai2023-10-29T14:00:00Z              4.537633\n",
              "Ezri Konsa Ngoyo2023-10-29T14:00:00Z                4.535804\n",
              "Moussa Diaby2023-10-29T14:00:00Z                    4.516049\n",
              "Emiliano Martínez Romero2023-10-29T14:00:00Z        4.385442\n",
              "Dejan Kulusevski2023-10-27T19:00:00Z                4.312208\n",
              "Douglas Luiz Soares de Paulo2023-10-29T14:00:00Z    4.309176\n",
              "John McGinn2023-10-29T14:00:00Z                     4.303202\n",
              "Pascal Groß2023-10-29T14:00:00Z                     4.245943\n",
              "Phil Foden2023-10-29T15:30:00Z                      4.234619\n",
              "Andrew Robertson2023-10-29T14:00:00Z                4.213994\n",
              "David Raya Martin2023-10-28T14:00:00Z               4.204943\n",
              "Darwin Núñez Ribeiro2023-10-29T14:00:00Z            4.181486\n",
              "James Ward-Prowse2023-10-29T13:00:00Z               4.148899\n",
              "Kai Havertz2023-10-28T14:00:00Z                     4.130318\n",
              "Matty Cash2023-10-29T14:00:00Z                      4.123158\n",
              "Tomáš Souček2023-10-29T13:00:00Z                    4.102016\n",
              "Declan Rice2023-10-28T14:00:00Z                     4.045454\n",
              "Abdoulaye Doucouré2023-10-29T13:00:00Z              4.026932\n",
              "Raheem Sterling2023-10-28T11:30:00Z                 4.004489\n",
              "Vladimír Coufal2023-10-29T13:00:00Z                 3.972972\n",
              "Alisson Ramses Becker2023-10-29T14:00:00Z           3.967894\n",
              "Cristian Romero2023-10-27T19:00:00Z                 3.945540\n",
              "Mathias Jensen2023-10-28T11:30:00Z                  3.935375\n",
              "Joël Veltman2023-10-29T14:00:00Z                    3.920138\n",
              "Benjamin White2023-10-28T14:00:00Z                  3.919942\n",
              "Gabriel dos Santos Magalhães2023-10-28T14:00:00Z    3.916704\n",
              "Julián Álvarez2023-10-29T15:30:00Z                  3.875503\n",
              "Kurt Zouma2023-10-29T13:00:00Z                      3.830288\n",
              "Leandro Trossard2023-10-28T14:00:00Z                3.824626\n",
              "Norberto Murara Neto2023-10-28T14:00:00Z            3.812593\n",
              "Virgil van Dijk2023-10-29T14:00:00Z                 3.812125\n",
              "Bruno Borges Fernandes2023-10-29T15:30:00Z          3.783217\n",
              "Name: points, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "test[\"points\"].sort_values(ascending=False).head(50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "TEVZdPSFrxKn"
      },
      "outputs": [],
      "source": [
        "\n",
        "feature_importance = pd.DataFrame(\n",
        "    {\"column\": x.columns, \"imp\": model.feature_importances_}\n",
        ").sort_values(\n",
        "    \"imp\", ascending=False\n",
        ")  #"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cp1yUjTNvJnk",
        "outputId": "76a00ccf-df81-46e9-b3bc-58527fbefefb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             column    imp\n",
              "71    opp mean team Goal scored all  13260\n",
              "72  opp mean team Goal conceded all  12240\n",
              "73        opp mean match_result all  12043\n",
              "64        mean team Goal scored all  10330\n",
              "62            mean match_result all  10148\n",
              "66      mean team Goal conceded all   9773\n",
              "4                     percent_value   9079\n",
              "30                 mean influence 3   7914\n",
              "41                  std influence 3   7710\n",
              "40                  std ict_index 3   7637\n",
              "39                 std creativity 3   7578\n",
              "52          mean goals_conceded all   7534\n",
              "38                        std bps 3   7484\n",
              "43                     std threat 3   7460\n",
              "26                mean creativity 3   7418\n",
              "21    opponent_last_season_position   7306\n",
              "29                 mean ict_index 3   7171\n",
              "77                             week   6988\n",
              "24                       mean bps 3   6928\n",
              "20                               GW   6841\n",
              "55               mean influence all   6814\n",
              "59                  mean threat all   6740\n",
              "34                    mean threat 3   6733\n",
              "50            mean clean_sheets all   6698\n",
              "51              mean creativity all   6290\n",
              "54               mean ict_index all   6284\n",
              "49                     mean bps all   6214\n",
              "60            mean total_points all   6188\n",
              "48                   mean bonus all   6105\n",
              "70    opp mean team Goal conceded 3   6060\n",
              "69      opp mean team Goal scored 3   5964\n",
              "56                 mean minutes all   5962\n",
              "61                   mean value all   5595\n",
              "31                   mean minutes 3   5563\n",
              "3              last_season_position   5504\n",
              "63          mean team Goal scored 3   5263\n",
              "76                             hour   5206\n",
              "36                     mean value 3   5156\n",
              "44               std total_points 3   5131\n",
              "11                    creativity_ex   5055\n",
              "67            ratio_goal_scored all   4929\n",
              "19                      now_cost_ex   4865\n",
              "9                        minutes_ex   4816\n",
              "10                goals_conceded_ex   4778\n",
              "15                           bps_ex   4742\n",
              "42                    std minutes 3   4680\n",
              "0                             value   4660\n",
              "8                   total_points_ex   4627\n",
              "47                 mean assists all   4606\n",
              "13                        threat_ex   4594"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3e727362-c366-4c7c-982a-99727a48ee02\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>column</th>\n",
              "      <th>imp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>opp mean team Goal scored all</td>\n",
              "      <td>13260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>opp mean team Goal conceded all</td>\n",
              "      <td>12240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>opp mean match_result all</td>\n",
              "      <td>12043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>mean team Goal scored all</td>\n",
              "      <td>10330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>mean match_result all</td>\n",
              "      <td>10148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>mean team Goal conceded all</td>\n",
              "      <td>9773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>percent_value</td>\n",
              "      <td>9079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>mean influence 3</td>\n",
              "      <td>7914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>std influence 3</td>\n",
              "      <td>7710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>std ict_index 3</td>\n",
              "      <td>7637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>std creativity 3</td>\n",
              "      <td>7578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>mean goals_conceded all</td>\n",
              "      <td>7534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>std bps 3</td>\n",
              "      <td>7484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>std threat 3</td>\n",
              "      <td>7460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>mean creativity 3</td>\n",
              "      <td>7418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>opponent_last_season_position</td>\n",
              "      <td>7306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>mean ict_index 3</td>\n",
              "      <td>7171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>week</td>\n",
              "      <td>6988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>mean bps 3</td>\n",
              "      <td>6928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>GW</td>\n",
              "      <td>6841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>mean influence all</td>\n",
              "      <td>6814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>mean threat all</td>\n",
              "      <td>6740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>mean threat 3</td>\n",
              "      <td>6733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>mean clean_sheets all</td>\n",
              "      <td>6698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>mean creativity all</td>\n",
              "      <td>6290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>mean ict_index all</td>\n",
              "      <td>6284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>mean bps all</td>\n",
              "      <td>6214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>mean total_points all</td>\n",
              "      <td>6188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>mean bonus all</td>\n",
              "      <td>6105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>opp mean team Goal conceded 3</td>\n",
              "      <td>6060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>opp mean team Goal scored 3</td>\n",
              "      <td>5964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>mean minutes all</td>\n",
              "      <td>5962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>mean value all</td>\n",
              "      <td>5595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>mean minutes 3</td>\n",
              "      <td>5563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>last_season_position</td>\n",
              "      <td>5504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>mean team Goal scored 3</td>\n",
              "      <td>5263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>hour</td>\n",
              "      <td>5206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>mean value 3</td>\n",
              "      <td>5156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>std total_points 3</td>\n",
              "      <td>5131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>creativity_ex</td>\n",
              "      <td>5055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>ratio_goal_scored all</td>\n",
              "      <td>4929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>now_cost_ex</td>\n",
              "      <td>4865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>minutes_ex</td>\n",
              "      <td>4816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>goals_conceded_ex</td>\n",
              "      <td>4778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>bps_ex</td>\n",
              "      <td>4742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>std minutes 3</td>\n",
              "      <td>4680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>value</td>\n",
              "      <td>4660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>total_points_ex</td>\n",
              "      <td>4627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>mean assists all</td>\n",
              "      <td>4606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>threat_ex</td>\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "feature_importance.head(50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "oB09ezWX0Q6H",
        "outputId": "4aeae781-1788-4dcc-feaa-c9577d8aaea0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       column   imp\n",
              "47           mean assists all  4606\n",
              "13                  threat_ex  4594\n",
              "12               influence_ex  4489\n",
              "35        mean total_points 3  4473\n",
              "16               ict_index_ex  4352\n",
              "65  mean team Goal conceded 3  4266\n",
              "53      mean goals_scored all  3896\n",
              "37        mean match_result 3  3873\n",
              "27      mean goals_conceded 3  3276\n",
              "14                   bonus_ex  3126\n",
              "5               position rank  3105\n",
              "17            clean_sheets_ex  3048\n",
              "18            yellow_cards_ex  2859\n",
              "75                      month  2754\n",
              "7                  assists_ex  2454\n",
              "68        ratio_goal_scored 3  2304\n",
              "1                    position  2169\n",
              "6             goals_scored_ex  2164\n",
              "2                    was_home  1642\n",
              "25        mean clean_sheets 3  1341\n",
              "46               mean saves 3  1020\n",
              "22             mean assists 3  1001\n",
              "23               mean bonus 3   994\n",
              "45                std value 3   971\n",
              "57         mean own_goals all   933\n",
              "58         mean red_cards all   778\n",
              "28        mean goals_scored 3   329\n",
              "32           mean own_goals 3     8\n",
              "33           mean red_cards 3     0\n",
              "74                day_of week     0"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>column</th>\n",
              "      <th>imp</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>mean assists all</td>\n",
              "      <td>4606</td>\n",
              "    </tr>\n",
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              "      <th>13</th>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>influence_ex</td>\n",
              "      <td>4489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>mean total_points 3</td>\n",
              "      <td>4473</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>ict_index_ex</td>\n",
              "      <td>4352</td>\n",
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              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>mean team Goal conceded 3</td>\n",
              "      <td>4266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>mean goals_scored all</td>\n",
              "      <td>3896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>mean match_result 3</td>\n",
              "      <td>3873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>mean goals_conceded 3</td>\n",
              "      <td>3276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>bonus_ex</td>\n",
              "      <td>3126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>position rank</td>\n",
              "      <td>3105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>clean_sheets_ex</td>\n",
              "      <td>3048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>yellow_cards_ex</td>\n",
              "      <td>2859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>month</td>\n",
              "      <td>2754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>assists_ex</td>\n",
              "      <td>2454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>ratio_goal_scored 3</td>\n",
              "      <td>2304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>position</td>\n",
              "      <td>2169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>goals_scored_ex</td>\n",
              "      <td>2164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>was_home</td>\n",
              "      <td>1642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>mean clean_sheets 3</td>\n",
              "      <td>1341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>mean saves 3</td>\n",
              "      <td>1020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>mean assists 3</td>\n",
              "      <td>1001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>mean bonus 3</td>\n",
              "      <td>994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>std value 3</td>\n",
              "      <td>971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>mean own_goals all</td>\n",
              "      <td>933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>mean red_cards all</td>\n",
              "      <td>778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>mean goals_scored 3</td>\n",
              "      <td>329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>mean own_goals 3</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>mean red_cards 3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>day_of week</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7fd5191-b840-40f9-8183-efae9d5f182a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f7fd5191-b840-40f9-8183-efae9d5f182a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f7fd5191-b840-40f9-8183-efae9d5f182a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a9c69860-80c3-4197-ad60-237231472eab\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a9c69860-80c3-4197-ad60-237231472eab')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a9c69860-80c3-4197-ad60-237231472eab button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "feature_importance.tail(30)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GBl8UYussJIl"
      },
      "source": [
        "#Save predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "HRm_1_f0sb1d"
      },
      "outputs": [],
      "source": [
        "test_copy[test_copy[\"position\"]==\"DEF\"][[\"name\",\"team\",\"minutes\"]].to_csv(f\"/content/drive/MyDrive/Fplpredict /predicted_dataset/GW{gameweek}/defenders_minutes.csv\")\n",
        "test_copy[test_copy[\"position\"]==\"GKP\"][[\"name\",\"team\",\"minutes\"]].to_csv(f\"/content/drive/MyDrive/Fplpredict /predicted_dataset/GW{gameweek}/goalkeepers_minutes.csv\")\n",
        "test_copy[test_copy[\"position\"]==\"MID\"][[\"name\",\"team\",\"minutes\"]].to_csv(f\"/content/drive/MyDrive/Fplpredict /predicted_dataset/GW{gameweek}/midfielders_minutes.csv\")\n",
        "test_copy[test_copy[\"position\"]==\"FWD\"][[\"name\",\"team\",\"minutes\"]].to_csv(f\"/content/drive/MyDrive/Fplpredict /predicted_dataset/GW{gameweek}/forwards_minutes.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "jGCIVTg1uCs3"
      },
      "outputs": [],
      "source": [
        "test[test[\"position\"]==\"DEF\"][[\"name\",\"team\",\"points\",\"value\"]].to_csv(f\"/content/drive/MyDrive/Fplpredict /predicted_dataset/GW{gameweek}/defenders_points.csv\")\n",
        "test[test[\"position\"]==\"GKP\"][[\"name\",\"team\",\"points\",\"value\"]].to_csv(f\"/content/drive/MyDrive/Fplpredict /predicted_dataset/GW{gameweek}/goalkeepers_points.csv\")\n",
        "test[test[\"position\"]==\"MID\"][[\"name\",\"team\",\"points\",\"value\"]].to_csv(f\"/content/drive/MyDrive/Fplpredict /predicted_dataset/GW{gameweek}/midfielders_points.csv\")\n",
        "test[test[\"position\"]==\"FWD\"][[\"name\",\"team\",\"points\",\"value\"]].to_csv(f\"/content/drive/MyDrive/Fplpredict /predicted_dataset/GW{gameweek}/forwards_points.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "G_OjzKsigsv_"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1aEyJ1htjtEffPwyNX1iNXKt1AcmE_aIT",
      "authorship_tag": "ABX9TyPQfJgzw5l5i50tJbZklE/V",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}